There are a variety of mapping and localization technologies available for mobile devices. By way of example, some mobile phones can determine their location based on data received from Global Positioning System (GPS) satellites. The location information can then be used in a variety of different applications. In some implementations, for example, a message or reminder is sent to the mobile device based on the location of the mobile device.
GPS-based mapping systems, however, tend to be less effective in indoor environments, since the walls and ceilings of a building may block the GPS signals. There have been various efforts to develop localization systems that are effective inside of a building.
One such approach is commonly referred to as fingerprinting. In some approaches, multiple WiFi access points are set up in an area in which localization is desired. Then, the area is divided into multiple regions. At each region, test signals from the WiFi access points are received and their respective signal strengths are stored in a database. Each region in the area is thus associated with a particular signal strength pattern. If greater accuracy is desired in the localization process, a greater number of signal strength patterns must be collected.
Localization is possible once the above fingerprinting process is completed. That is, when a mobile device user enters the area and moves to a particular location, the mobile device assesses the strength of the signals received from the WiFi access points. This signal strength pattern is matched with the ones in the database to determine the location of the mobile device.
Fingerprinting, while effective in some applications, also has several disadvantages. For one, each localization area must be separately fingerprinted. This requires substantial amounts of time and effort. Additionally, signal noise (e.g., from other WiFi, FM radio or Bluetooth sources) can reduce the accuracy of the localization process.
Some other localization techniques involve measuring a signal pattern while the mobile device user is moving. Generally, such approaches require the movement of the mobile device user to match a tested movement pattern or trajectory. If the mobile device user, however, moves in an irregular manner or along a path that is different from the tested movement patterns, such localization techniques tend to be less accurate.